Agentic AI Skills Curriculum for IT Professionals – DreamsPlus
Build Autonomous AI Systems That Perceive, Reason, Plan & Act
DreamsPlus offers an industry-aligned Agentic AI Training in Chennai designed to meet the rapidly growing demand for autonomous, goal-driven AI systems. This program focuses on building Agentic AI capabilities—systems that can independently reason, plan, collaborate, and take actions using tools and APIs.
This Agentic AI course in Chennai is tailored specifically for existing IT professionals across multiple roles, enabling them to design, deploy, and scale enterprise-grade agentic AI solutions.
Why Agentic AI Training Is Demand Today
Modern enterprises are rapidly adopting autonomous AI agent systems to automate workflows, decision-making, and operations. Unlike basic AI models, Agentic AI systems can think, reason, collaborate, and execute tasks across multiple tools and platforms.
This Agentic AI training for IT professionals prepares you to design, deploy, and manage intelligent agents used in:
AI automation platforms
Enterprise AI solutions
Conversational AI systems
Multi-agent orchestration frameworks
Autonomous business process automation
Why Choose DreamsPlus for an Agentic AI Course in Chennai?
- Trusted AI training institute in Chennai
- Industry-aligned curriculum
- Enterprise-level projects
- Expert trainers
- Career-focused learning paths
Agentic AI Course Curriculum
AI Core Foundation (All Roles) - 40 Hours
Module 1: Agentic AI Fundamentals (16 hours)
Learning Objectives:
Understand agent architectures (ReAct, AutoGPT, BabyAGI patterns)
Differentiate between traditional AI, generative AI, and agentic systems
Grasp multi-agent systems and orchestration patterns
Topics:
What makes AI “agentic”: autonomy, goal-orientation, tool use
Agent reasoning frameworks (Chain-of-Thought, Tree-of-Thought, ReAct)
Memory systems (short-term, long-term, episodic)
Tool integration and function calling
Agent evaluation and safety considerations
Hands-on Project: Build a simple task-planning agent using LangChain or AutoGen
Module 2: LLM Operations Essentials (12 hours)
Topics:
Prompt engineering for agentic behavior
Context window management and retrieval strategies
Model selection criteria (GPT-4, Claude, Gemini, open-source)
API usage, rate limiting, and cost optimization
Error handling and fallback strategies
Lab: Implement prompt templates and context management for agent workflows
Module 3: RAG and Knowledge Systems (12 hours)
Topics:
Vector databases (Pinecone, Weaviate, Chroma)
Embedding models and semantic search
Chunking strategies and metadata management
Hybrid search approaches
Knowledge graph integration
Lab:
Build a RAG system that an agent can query dynamically
Role-Specific Tracks
Track 1: Software Engineers /Developers - 80 Hours
Prerequisite: Core AI Foundation Modules 1 to 3 Mandatory
Module 4: Agent Development Frameworks (24 hours)
Skills:
- LangChain agents and chains
- AutoGen multi-agent conversations
- CrewAI for role-based agents
- LlamaIndex for data agents
- Semantic Kernel for enterprise integration
Projects:
- Multi-step research agent with tool use
- Code generation and debugging agent
- Customer service agent with escalation logic
Module 5: Tool Integration & APIs (16 hours)
Skills:
- Function calling and tool schemas
- API wrapper development
- Browser automation (Playwright, Selenium)
- Database query generation
- Third-party service integration (Slack, GitHub, Jira)
Project: Build an agent that automates developer workflows across multiple tools
Module 6: Agent Testing & Reliability (16 hours)
Skills:
- Unit testing for non-deterministic systems
- Evaluation frameworks (LangSmith, Phoenix)
- Human-in-the-loop patterns
- Guardrails and output validation
- Observability and logging
Project: Implement comprehensive testing suite for an agentic application
Module 7: Production Deployment (24 hours)
- Containerization for agent systems
- Streaming responses and async patterns
- State management and persistence
- Scaling agent workloads
- Monitoring and alerting
Track 2: Data Engineers - 70 Hours
Module 4: Data Pipeline Agents (20 hours)
- Automated data quality agents
- Schema inference and transformation
- Metadata extraction and cataloging
- Anomaly detection agents
- Self-healing data pipelines
Module 5: Agentic ETL & Orchestration (20 hours)
- Dynamic pipeline generation
- Intelligent data routing
- Agent-based workflow optimization
- Integration with Airflow/Prefect
- Data lineage tracking
Module 6: Vector Data Infrastructure (16 hours)
- Vector database architecture and scaling
- Embedding pipeline design
- Incremental indexing strategies
- Multi-modal embeddings
- Performance optimization
Module 7: Agent Data Governance (14 hours)
- PII detection and redaction
- Access control for agent queries
- Audit logging for agent decisions
- Data versioning for agent training
- Compliance monitoring
Track 3: DevOps/Platform Engineers - 70 Hours
Module 4: Agent Infrastructure (20 hours)
Skills:
- Kubernetes for agent workloads
- Serverless patterns (Lambda, Cloud Run)
- Message queues for agent communication
- Resource management and auto-scaling
- Multi-region deployment strategies
Projects:
Build scalable infrastructure for agent execution
Module 5: MLOps for Agents (18 hours)
Skills:
- Model versioning and deployment
- A/B testing for agent behaviors
- Feature flags for agent capabilities
- Rollback strategies
- Performance benchmarking
Project: Implement CI/CD pipeline for agent deployments
Module 6: Observability & Monitoring (16 hours)
Skills:
- Distributed tracing for agent decisions
- Token usage and cost tracking
- Latency monitoring and optimization
- Custom metrics for agent performance
- Alerting for agent failures
Project: Implement Build comprehensive observability stack for agent systems
Module 7: Security & Compliance (16 hours)
Skills:
- Prompt injection defense
- Secret management for API keys
- Network isolation for agent workloads
- Compliance automation (SOC2, GDPR)
- Security scanning for agent code
Capstone: Secure production deployment of multi-agent system
Track 4: System Architects - 75 Hours
Module 4: Agent Architecture Patterns (24 hours)
- Single-agent vs multi-agent architectures
- Orchestration patterns (hierarchical, flat, dynamic)
- Agent communication protocols
- State management strategies
- Hybrid human-agent workflows
Module 5: System Integration Design (20 hours)
- Legacy system integration strategies
- Event-driven agent architectures
- Service mesh for agent communication
- API gateway patterns for agents
- Microservices decomposition
Module 6: Scalability & Performance (16 hours)
- Load balancing for agent requests
- Caching strategies for agent responses
- Database optimization for agent state
- Async processing patterns
- Resource allocation modeling
Module 7: Enterprise AI Governance (15 hours)
- AI risk management frameworks
- Ethical AI guidelines implementation
- Bias detection and mitigation
- Explainability requirements
- Vendor evaluation criteria
Track 5: QA/Test Engineers - 65 Hours
Module 4: Agent Testing Strategies (20 hours)
Skills:
- Behavioral testing for non-deterministic systems
- Test case generation for agent workflows
- Synthetic data creation
- Regression testing approaches
- Edge case identification
Projects:
Build scalable infrastructure for agent execution
Module 5: Evaluation Frameworks (18 hours)
Skills:
- LLM-as-judge evaluation patterns
- Human evaluation workflows
- Automated scoring systems
- Benchmark dataset creation
- A/B testing methodologies
Project: Automated evaluation pipeline for agent performance
Module 6: Reliability Engineering (15 hours)
Skills:
- Failure mode analysis for agents
- Chaos engineering for agent systems
- Recovery testing
- Failover validation
- Load testing for agent infrastructure
Project: Reliability testing framework for production agents
Module 7: Quality Metrics (12 hours)
Skills:
- Success rate measurement
- Response quality scoring
- User satisfaction metrics
- Cost-effectiveness analysis
- Continuous improvement processes
Capstone: Quality assurance framework for enterprise agent deployment
Track 6: Business Analysts /Product Managers - 55 Hours
Module 4: Agent Use Case Design (16 hours)
- Identifying automation opportunities
- User journey mapping for agent interactions
- ROI calculation for agent implementations
- Requirements gathering for agent capabilities
- Prioritization frameworks
Module 5: Agent UX Design (14 hours)
- Conversational interface design
- Transparency and explainability in UX
- Human handoff patterns
- Feedback collection mechanisms
- Accessibility considerations
Module 6: Agent Performance Management (12 hours)
- KPI definition for agent systems
- Success metrics and dashboards
- User acceptance criteria
- Continuous feedback loops
- Iterative improvement processes
Module 7: Stakeholder Management (13 hours)
- Communicating AI capabilities and limitations
- Managing expectations
- Risk communication
- Change management for AI adoption
- Cross-functional collaboration
Advanced Specializations (Optional) - 40 Hours Each
Specialization A: Multi-Agent Systems
- Game theory for agent cooperation
- Consensus mechanisms
- Agent negotiation protocols
- Swarm intelligence patterns
- Distributed decision-making
Specialization B: Autonomous Code Agents
- Code generation and refactoring
- Automated testing generation
- Bug detection and fixing
- Documentation generation
- Repository analysis and recommendations
Specialization C: Enterprise Integration
- SAP, Salesforce, ServiceNow integration
- Legacy system modernization with agents
- Data migration automation
- Process mining and optimization
- Robotic Process Automation (RPA) + AI
Specialization D: Vertical AI Agents
- Healthcare: clinical decision support agents
- Finance: fraud detection and compliance agents
- Legal: contract analysis and review agents
- Manufacturing: predictive maintenance agents
- Retail: inventory and demand planning agents
Learning Methodology
Delivery Format:
- 60% hands-on labs and projects
- 25% theory and conceptual learning
- 15% case studies and discussion
Assessment Methods
- Weekly coding assignments
- Project-based evaluations
- Peer code reviews
- Capstone project presentation
- Certification exam (optional)
Tools and Platforms
- GitHub for code repositories
- Jupyter notebooks for experimentation
- Cloud platforms (AWS/Azure/GCP) sandboxes
- LangSmith or LangFuse for agent observability
- Slack/Discord for community learning
Prerequisites by Role
All Roles:
- Basic Python programming
- Understanding of REST APIs
- Familiarity with cloud services
- Version control (Git)
Additional Prerequisites:
- Developers: Object-oriented programming, async/await patterns
- Data Engineers: SQL, data modeling, ETL concepts
- DevOps: Container orchestration, CI/CD, infrastructure as code
- Architects: Distributed systems, microservices, system design
- QA: Testing frameworks, automation tools
- Business Analysts: Requirements gathering, process mapping
Certification Path
Level 1: Agentic AI Practitioner (Foundation + 1 Track)
- Demonstrates ability to build and deploy basic agents
- Understands agentic AI principles and patterns
Level 2: Agentic AI Specialist (Level 1 + Specialization)
- Expert in specific domain of agentic AI
- Can architect complex multi-agent systems
Level 3: Agentic AI Architect (Multiple tracks + Real-world project)
- Enterprise-level design and implementation
- Thought leadership and team mentorship
Recommended Learning Timeline
- Foundation: 2 weeks
- Role-specific: 8-10 weeks
- Capstone: 2-4 weeks
- 10-15 hours per week commitment
- Self-paced with milestone deadlines
- Cohort-based for peer learning
- AI automation specialists
- Agent orchestration engineers
- LLM operations (LLMOps) engineers
- Conversational AI developers
- AI integration architects
- Autonomous system designers
- Skills align with job requirements from companies building agentic AI platforms, enterprise AI solutions, and autonomous business process automation
Course Highlights – Agentic AI Course in Chennai
Industry-oriented Agentic AI training in Chennai
Hands-on Agentic AI certification program
Real-world autonomous AI agent projects
Role-specific learning paths
Enterprise tools: LangChain, AutoGen, CrewAI, LLMOps
Production deployment & monitoring
Designed for developers, data engineers, DevOps, architects, QA & BAs
Certification Path
Levels
- Level 1: Agentic AI Practitioner
- Level 2: Agentic AI Specialist
- Level 3: Agentic AI Architect
Recommended Learning Timeline
- Intensive Track: 12–16 weeks
- Part-Time Track: 6–9 months
- Cohort-based & milestone-driven learning
Industry Alignment & Career Outcomes
- AI Automation Specialists
- Agent Orchestration Engineers
- LLMOps Engineers
- Conversational AI Developers
- AI Integration Architects
- Autonomous System Designers
Disclaimer
All salary figures, job statistics, adoption rates, and quantitative data mentioned on this page are sourced from publicly available web resources, industry reports, and prevailing market trends. DreamsPlus does not guarantee the accuracy, completeness, or timeliness of this information.
Actual salaries, job opportunities, certification outcomes, and career growth may vary based on individual skills, experience, location, organization, and market conditions. This information is provided strictly for general guidance and reference purposes only. Learners are advised to independently verify all data before making career, financial, or educational decisions.
Frequently Asked Question
Agentic AI training in Chennai focuses on building autonomous AI agents that can reason, plan, use tools, and take actions independently. This course teaches professionals how to design, deploy, and manage agentic AI systems using technologies like LangChain, AutoGen, RAG, and multi-agent orchestration for real-world enterprise applications.
The Agentic AI course in Chennai is designed for IT professionals such as software developers, data engineers, DevOps engineers, system architects, QA testers, and business analysts. Anyone with basic Python knowledge and an interest in AI automation or autonomous systems can benefit from this training.
Through Agentic AI certification training, you will gain skills in autonomous AI agent development, multi-agent systems, LLMOps, RAG systems, vector databases, agent testing, security, and production deployment. You will also learn how to integrate AI agents with enterprise tools and cloud platforms.
Yes, this Agentic AI training in Chennai includes extensive hands-on learning with real-world projects. Participants will build task-planning agents, multi-agent systems, RAG-based AI agents, and production-ready agentic applications, with capstone projects aligned to enterprise use cases.
After completing the Agentic AI course in Chennai, learners can pursue roles such as Agentic AI Engineer, AI Automation Specialist, LLMOps Engineer, AI Integration Architect, Autonomous Systems Designer, and Conversational AI Developer across industries adopting AI-driven automation.